Incorporate profvis or another performance visualization tool to identify bottlenecks. From my basic research, it looks like profvis runs locally.
In an ideal situation, we would integrate performance telemetry with the app as a whole that would run when it was deployed, so we would understand what the actual performance is for all users and all queries (and not just what performance was for a single user running a couple of different queries locally).
For a "regular" Javascript web app deployed on Azure, you could use a service like Azure Monitoring or Application Insights to get this with minimal extra work. On the RShiny side, I think we can take advantage of the shiny.telemetry package to get what we need.
Incorporate profvis or another performance visualization tool to identify bottlenecks. From my basic research, it looks like profvis runs locally.
In an ideal situation, we would integrate performance telemetry with the app as a whole that would run when it was deployed, so we would understand what the actual performance is for all users and all queries (and not just what performance was for a single user running a couple of different queries locally).
For a "regular" Javascript web app deployed on Azure, you could use a service like Azure Monitoring or Application Insights to get this with minimal extra work. On the RShiny side, I think we can take advantage of the shiny.telemetry package to get what we need.
Profvis: https://github.com/r-lib/profvis Shiny.telemetry: https://github.com/Appsilon/shiny.telemetry